Linguistic Distancing on Social Media: Indicators of Emotion Regulation Across Age Groups

Alona Fyshe; Daniela Teodorescu; Saif M. Mohammad

arxiv: 2606.30957 · v1 · pith:4IG63YZH · submitted 2026-06-29 · cs.CL

Linguistic Distancing on Social Media: Indicators of Emotion Regulation Across Age Groups

Daniela Teodorescu , Saif M. Mohammad , Alona Fyshe This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-07-01 01:27 UTCgrok-4.3pith:4IG63YZH record.json open to challenge →

classification cs.CL

keywords linguistic distancingemotion regulationsocial mediaage groupswell-beingtext analysis

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The pith

Linguistic distancing occurs in proportionally more instances with age in social media text.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines linguistic distancing, a set of language markers that indicate psychological distance from events, as a form of emotion regulation. It applies an existing metric to large collections of social media posts to measure how often these markers appear across different age groups. The analysis finds that the proportion of posts showing distancing rises steadily with age. This pattern matches established psychological observations that emotional well-being tends to improve later in life and supplies initial reference values for text-based studies of regulation on social platforms.

Core claim

By applying a prior operationalization of linguistic distancing to social media text, the authors show that distancing markers appear in a higher proportion of posts as user age increases, supplying further evidence for an age-related increase in this aspect of emotion regulation.

What carries the argument

Linguistic distancing, operationalized through specific textual markers that signal psychological distance from described events and treated as an indicator of emotion regulation.

Load-bearing premise

The chosen metric correctly measures emotion regulation and ages derived from social media profiles carry no systematic bias in posting behavior.

What would settle it

A dataset in which ages are verified by external records and linguistic distancing scores show no positive relationship with age would falsify the central claim.

Figures

Figures reproduced from arXiv: 2606.30957 by Alona Fyshe, Daniela Teodorescu, Saif M. Mohammad.

**Figure 1.** Figure 1: Linguistic Distancing scores for TUSC-City, and Reddit subset of the AgeCorpus (blue line). Individual components of linguistic distancing across age groups are shown: temporal distance (orange line), social distance (green line), passive voice (red line) and abstractness (purple line). Error bars represent the standard error of the mean. 5. Results 5.1. RQ1: How Does Linguistic Distancing Vary Across Age … view at source ↗

**Figure 2.** Figure 2: Linguistic Distancing scores for TUSC-Country subset of the AgeCorpus (blue line). Individual components of linguistic distancing across age groups are shown: temporal distance (orange line), social distance (green line), passive voice (red line) and abstractness (purple line). Error bars represent the standard error of the mean [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗

read the original abstract

Managing our emotional responses to events is key to emotional well-being, a process referred to as emotion regulation in psychology. Previous work has established that the degree to which we distance events is a type of emotion regulation. When we psychologically distance from events there can be markers in our language. These markers have been referred to as linguistic distancing. We build upon a previous metric to operationalize linguistic distancing, and explore how it changes across the lifespan. We explore this systematically by analyzing large amounts of social media text, a venue where people express their emotions. By investigating how distancing varies across age groups we can better understand how emotion regulation varies with age and provide initial benchmarks on social media data. We provide additional evidence further strengthening the hypothesis that linguistic distancing occurs in proportionally more instances with age. These findings align with past work in psychology which indicate improved well-being with older age. Better understanding how linguistic distancing changes with age is important because it functions as a marker of well-being and can inform effective health interventions. We provide a foundation for further exploring emotion regulation through linguistic distancing in text data.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper applies an existing linguistic distancing metric to social media text and reports an age increase, but provides no details on age inference or confounds, so the central claim cannot be evaluated from the abstract.

read the letter

The paper takes a prior metric for linguistic distancing and runs it across age groups on social media posts, finding proportionally more distancing with age. This supplies one more data point consistent with psychology findings on emotion regulation improving over the lifespan.

It does a straightforward job of scaling up an established measure to a large real-world corpus and framing the output as initial benchmarks. That scale is the main practical plus.

The soft spots sit in the methods. The abstract gives no sample sizes, no description of how age was inferred from user data, and no mention of controls for platform effects or cohort differences in posting volume or topics. The stress-test note on age inference bias and potential artifacts is accurate based on what is shown; without those specifics the observed trend could reflect labeling choices or usage patterns rather than regulation changes. It is also explicitly additional evidence rather than a new result or framework.

This is for researchers working on computational markers of emotion or well-being in text who need age-stratified reference points. A reader in that niche would get some value from the corpus size and the alignment with prior psychology, but would have to wait for the full methods to judge reliability.

Send it to peer review if the full paper supplies clear age-inference procedures, sample statistics, and confound checks; otherwise it needs those sections fixed first.

Referee Report

2 major / 1 minor

Summary. The manuscript operationalizes linguistic distancing from prior work as a marker of emotion regulation and analyzes its occurrence in large volumes of social media text across age groups. It reports that distancing occurs in proportionally more instances with increasing age, providing additional evidence that strengthens the hypothesis of improved emotion regulation over the lifespan and aligns with psychological findings on well-being.

Significance. If the age-group assignments and measurements prove robust to confounds, the work would supply large-scale, naturalistic benchmarks from social media for lifespan changes in emotion regulation. This could complement lab-based psychology studies and support development of text-based health interventions. The scale of social media data is a potential strength for generalizability.

major comments (2)

[Methods] Methods section (data collection and age inference): No description is provided of the age-inference procedure, sample sizes, statistical controls, or handling of confounds such as platform effects or cohort-specific posting biases. This is load-bearing for the central claim, because systematic differences in how age cohorts use the platform could produce the observed aggregate increase in distancing without any change in emotion regulation.
[Abstract / Results] Abstract and Results: The claim that the analysis 'further strengthen[s] the hypothesis' is asserted without reported effect sizes, confidence intervals, or details on how the prior metric was adapted and validated on the new corpus. This prevents evaluation of whether the evidence is incremental or merely replicative.

minor comments (1)

[Abstract] The abstract refers to 'a previous metric' without a citation or brief recap of its formulation; adding this would improve accessibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful and constructive review. We address each major comment below and commit to revisions that strengthen the manuscript's transparency and rigor.

read point-by-point responses

Referee: [Methods] Methods section (data collection and age inference): No description is provided of the age-inference procedure, sample sizes, statistical controls, or handling of confounds such as platform effects or cohort-specific posting biases. This is load-bearing for the central claim, because systematic differences in how age cohorts use the platform could produce the observed aggregate increase in distancing without any change in emotion regulation.

Authors: We agree that the Methods section requires substantial expansion to support the central claims. The initial submission omitted these details primarily due to length constraints. In the revised manuscript we will add: (1) a full description of the age-inference procedure (including data sources and validation), (2) exact sample sizes and demographic breakdowns per age group, (3) the statistical models and controls employed (e.g., regression specifications that account for post length, topic, and platform usage patterns), and (4) an explicit discussion of potential confounds such as cohort-specific posting biases and platform effects, together with any sensitivity analyses conducted to evaluate their influence on the observed age trend. revision: yes
Referee: [Abstract / Results] Abstract and Results: The claim that the analysis 'further strengthen[s] the hypothesis' is asserted without reported effect sizes, confidence intervals, or details on how the prior metric was adapted and validated on the new corpus. This prevents evaluation of whether the evidence is incremental or merely replicative.

Authors: We accept that the strengthening claim needs quantitative backing. The revised manuscript will report effect sizes (e.g., standardized coefficients or odds ratios for the age-group trend), 95% confidence intervals around key proportions, and a new subsection detailing the adaptation of the linguistic-distancing metric, including any modifications made for the social-media corpus and validation procedures (such as correlation with human annotations on a held-out sample). These additions will clarify the incremental contribution relative to prior work. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical measurement on new data with no fitted predictions or self-referential derivations

full rationale

The paper reports an empirical study that applies an existing operationalization of linguistic distancing to new social media text and measures its variation across age groups. No equations, parameters, or first-principles derivations are described; the central claim is simply an observed statistical pattern on fresh data that is presented as strengthening a prior hypothesis. The text contains no self-definitional steps, fitted-input predictions, or load-bearing self-citations that reduce the reported result to its own inputs by construction. This is a standard observational analysis whose validity rests on data collection and measurement choices rather than any circular derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Only abstract available; no free parameters, axioms, or invented entities are described. The work relies on the unstated assumption that social media language faithfully reflects internal emotion regulation processes.

axioms (2)

domain assumption Linguistic distancing metric from prior work validly measures emotion regulation.
Invoked when the authors operationalize the metric on new data.
domain assumption Age groups can be accurately identified from social media profiles or text.
Required to stratify results by age.

pith-pipeline@v0.9.1-grok · 5723 in / 1175 out tokens · 27971 ms · 2026-07-01T01:27:26.206217+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages · 1 internal anchor

[1]

Linguistic Distancing on Social Media: Indicators of Emotion Regulation Across Age Groups

Introduction Our everyday emotional experiences are not static butratherdynamicandouremotionsareconstantly changing over time. The way in which our emo- tional experiences change over time creates an emotional trajectory, or what some refer to as an emotion arc(Mohammad, 2011; Reagan et al., 2016). Emotion regulation includes the processes by which “we in...

work page internal anchor Pith review Pith/arXiv arXiv 2011
[2]

When psychologically distancing from an event, one is viewing the event from a third-person perspective, and often psychological distancing ap- pears in language as markers

and Dialectical Behavior Therapy (Linehan, 1993). When psychologically distancing from an event, one is viewing the event from a third-person perspective, and often psychological distancing ap- pears in language as markers. For example, when distancing, there are less first-person pronouns and more past and future tense verbs rather than present tense ver...

work page 1993
[3]

I”, “me”, “mine

Related Work Below we describe past work examining the rela- tionship between emotion regulation, linguistic dis- tancing and how they change across the lifespan. Afterwards, we describe markers of distancing in language. 2.1. Emotion Regulation, Linguistic Distancing & Age Appropriately regulating emotions is key to men- tal health and well-being. Vast a...

work page 1995
[4]

I am X years old

Dataset: AgeCorpus We perform our experiments onAgeCorpus, a so- cial media dataset containing posts annotated with the author’s age at the time of writing (Teodorescu et al., 2026). The dataset contains posts from both RedditandX,makingitasuitabledatasetforexplor- ing linguistic distance on social media platforms. Further, there are a large number of pos...

work page 2026
[5]

Experiments In the sections below we describe our methods for exploring how linguistic distancing varies across age groups. 4.1. Computing Linguistic Distancing in Text We build on Nook et al. (2022)’s work on comput- ing linguistic distancing in text. Specifically, the au- thors operationalize linguistic distancing as being composed of two components:soc...

work page 2022
[6]

RQ1: How Does Linguistic Distancing Vary Across Age Groups? In Figure 1, we show the trend of linguistic distanc- ing across age groups

Results 5.1. RQ1: How Does Linguistic Distancing Vary Across Age Groups? In Figure 1, we show the trend of linguistic distanc- ing across age groups. We see that regardless of the dataset (i.e., X or Reddit), linguistic distancing (blue line) occurs morefrequentlywith age. This upwards trend is consistent across age groups (ex- cept with a dip in the 30’s...

work page 2010
[7]

We use large social media datasets to systematically analyze how does linguistic distanc- ing vary across teens to 70’s on X and Reddit

Conclusion We study how linguistic distancing changes across adulthood to better understand emotion regulation with age. We use large social media datasets to systematically analyze how does linguistic distanc- ing vary across teens to 70’s on X and Reddit. We construct an interpretable measure of linguistic dis- tancing based on four dimensions:temporald...

work page
[8]

Bibliographical References MS Albert, J Wolfe, and G Lafleche. 1990. Differ- ences in abstraction ability with age.Psychology and aging, 5(1):94—100. Amelia Aldao, Susan Nolen-Hoeksema, and Su- sanne Schweizer. 2010. Emotion-regulation strategies across psychopathology: A meta- analytic review.Clinical Psychology Review, 30(2):217–237. Jennifer L. Altshul...

work page 1990
[9]

Marc Brysbaert, Amy Beth Warriner, and Victor Kuperman

The influence of age on emotion regulation strategies and psychological dis- tress.Behavioural and Cognitive Psychotherapy, 42(6):668–681. Marc Brysbaert, Amy Beth Warriner, and Victor Kuperman. 2014. Concreteness ratings for 40 thousand generally known english word lemmas. Behavior research methods, 46(3):904—911. Eugene Y. Chan and Sam J. Maglio. 2020a....

work page 2014
[10]

Molly Ireland, Kaitlin Adams, and Sean Farrell

Relation between emotion regulation and mental health: a meta-analysis review.Psycho- logical reports, 114(2):341—362. Molly Ireland, Kaitlin Adams, and Sean Farrell

work page
[11]

InPro- ceedings of the Eighth Workshop on Computa- tional Linguistics and Clinical Psychology, pages 76–88, Seattle, USA

Tracking mental health risks and coping strategies in healthcare workers’ online conver- sations across the COVID-19 pandemic. InPro- ceedings of the Eighth Workshop on Computa- tional Linguistics and Clinical Psychology, pages 76–88, Seattle, USA. Association for Computa- tional Linguistics. Derek M Isaacowitz. 2022. What do we know about aging and emoti...

work page 2022
[12]

Copyright - ©The Author(s) 2025

Understandinggenderandagedifferences in language use: cross-cultural insights from weibo and facebook.Humanities & Social Sci- ences Communications, 12(1):1667. Copyright - ©The Author(s) 2025. This work is published un- der http://creativecommons.org/licenses/by-nc- nd/4.0/ (the “License”). Notwithstanding the Pro- Quest Terms and Conditions, you may use...

work page 2025
[13]

Andrew J

Does mastering of abstract words decline with age?Educational Gerontology, 47(12):527– 542. Andrew J. Reagan, Lewis Mitchell, Dilan Kiley, Christopher M. Danforth, and Peter S. Dodds

work page
[14]

The emotional arcs of stories are domi- nated by six basic shapes.EPJ Data Science, 5(1):31. H. Andrew Schwartz, Johannes C. Eichstaedt, Mar- garet L. Kern, Lukasz Dziurzynski, Stephanie M. Ramones, Megha Agrawal, Achal Shah, Michal Kosinski, David Stillwell, Martin E. P. Seligman, and Lyle H. Ungar. 2013. Personality, gender, and age in the language of s...

work page 2013
[15]

Country” refers to TUSC-Country and “City

Emotion regulation and psychopathol- ogy.Annual review of clinical psychology, 11:379—405. Robert S Stawski, Martin J Sliwinski, David M Almeida, and Joshua M Smyth. 2008. Reported exposure and emotional reactivity to daily stres- sors: the roles of adult age and global perceived stress.Psychology and aging, 23(1):52—61. ArthurA.Stone,JosephE.Schwartz,Joa...

work page 2008

[1] [1]

Linguistic Distancing on Social Media: Indicators of Emotion Regulation Across Age Groups

Introduction Our everyday emotional experiences are not static butratherdynamicandouremotionsareconstantly changing over time. The way in which our emo- tional experiences change over time creates an emotional trajectory, or what some refer to as an emotion arc(Mohammad, 2011; Reagan et al., 2016). Emotion regulation includes the processes by which “we in...

work page internal anchor Pith review Pith/arXiv arXiv 2011

[2] [2]

When psychologically distancing from an event, one is viewing the event from a third-person perspective, and often psychological distancing ap- pears in language as markers

and Dialectical Behavior Therapy (Linehan, 1993). When psychologically distancing from an event, one is viewing the event from a third-person perspective, and often psychological distancing ap- pears in language as markers. For example, when distancing, there are less first-person pronouns and more past and future tense verbs rather than present tense ver...

work page 1993

[3] [3]

I”, “me”, “mine

Related Work Below we describe past work examining the rela- tionship between emotion regulation, linguistic dis- tancing and how they change across the lifespan. Afterwards, we describe markers of distancing in language. 2.1. Emotion Regulation, Linguistic Distancing & Age Appropriately regulating emotions is key to men- tal health and well-being. Vast a...

work page 1995

[4] [4]

I am X years old

Dataset: AgeCorpus We perform our experiments onAgeCorpus, a so- cial media dataset containing posts annotated with the author’s age at the time of writing (Teodorescu et al., 2026). The dataset contains posts from both RedditandX,makingitasuitabledatasetforexplor- ing linguistic distance on social media platforms. Further, there are a large number of pos...

work page 2026

[5] [5]

Experiments In the sections below we describe our methods for exploring how linguistic distancing varies across age groups. 4.1. Computing Linguistic Distancing in Text We build on Nook et al. (2022)’s work on comput- ing linguistic distancing in text. Specifically, the au- thors operationalize linguistic distancing as being composed of two components:soc...

work page 2022

[6] [6]

RQ1: How Does Linguistic Distancing Vary Across Age Groups? In Figure 1, we show the trend of linguistic distanc- ing across age groups

Results 5.1. RQ1: How Does Linguistic Distancing Vary Across Age Groups? In Figure 1, we show the trend of linguistic distanc- ing across age groups. We see that regardless of the dataset (i.e., X or Reddit), linguistic distancing (blue line) occurs morefrequentlywith age. This upwards trend is consistent across age groups (ex- cept with a dip in the 30’s...

work page 2010

[7] [7]

We use large social media datasets to systematically analyze how does linguistic distanc- ing vary across teens to 70’s on X and Reddit

Conclusion We study how linguistic distancing changes across adulthood to better understand emotion regulation with age. We use large social media datasets to systematically analyze how does linguistic distanc- ing vary across teens to 70’s on X and Reddit. We construct an interpretable measure of linguistic dis- tancing based on four dimensions:temporald...

work page

[8] [8]

Bibliographical References MS Albert, J Wolfe, and G Lafleche. 1990. Differ- ences in abstraction ability with age.Psychology and aging, 5(1):94—100. Amelia Aldao, Susan Nolen-Hoeksema, and Su- sanne Schweizer. 2010. Emotion-regulation strategies across psychopathology: A meta- analytic review.Clinical Psychology Review, 30(2):217–237. Jennifer L. Altshul...

work page 1990

[9] [9]

Marc Brysbaert, Amy Beth Warriner, and Victor Kuperman

The influence of age on emotion regulation strategies and psychological dis- tress.Behavioural and Cognitive Psychotherapy, 42(6):668–681. Marc Brysbaert, Amy Beth Warriner, and Victor Kuperman. 2014. Concreteness ratings for 40 thousand generally known english word lemmas. Behavior research methods, 46(3):904—911. Eugene Y. Chan and Sam J. Maglio. 2020a....

work page 2014

[10] [10]

Molly Ireland, Kaitlin Adams, and Sean Farrell

Relation between emotion regulation and mental health: a meta-analysis review.Psycho- logical reports, 114(2):341—362. Molly Ireland, Kaitlin Adams, and Sean Farrell

work page

[11] [11]

InPro- ceedings of the Eighth Workshop on Computa- tional Linguistics and Clinical Psychology, pages 76–88, Seattle, USA

Tracking mental health risks and coping strategies in healthcare workers’ online conver- sations across the COVID-19 pandemic. InPro- ceedings of the Eighth Workshop on Computa- tional Linguistics and Clinical Psychology, pages 76–88, Seattle, USA. Association for Computa- tional Linguistics. Derek M Isaacowitz. 2022. What do we know about aging and emoti...

work page 2022

[12] [12]

Copyright - ©The Author(s) 2025

Understandinggenderandagedifferences in language use: cross-cultural insights from weibo and facebook.Humanities & Social Sci- ences Communications, 12(1):1667. Copyright - ©The Author(s) 2025. This work is published un- der http://creativecommons.org/licenses/by-nc- nd/4.0/ (the “License”). Notwithstanding the Pro- Quest Terms and Conditions, you may use...

work page 2025

[13] [13]

Andrew J

Does mastering of abstract words decline with age?Educational Gerontology, 47(12):527– 542. Andrew J. Reagan, Lewis Mitchell, Dilan Kiley, Christopher M. Danforth, and Peter S. Dodds

work page

[14] [14]

The emotional arcs of stories are domi- nated by six basic shapes.EPJ Data Science, 5(1):31. H. Andrew Schwartz, Johannes C. Eichstaedt, Mar- garet L. Kern, Lukasz Dziurzynski, Stephanie M. Ramones, Megha Agrawal, Achal Shah, Michal Kosinski, David Stillwell, Martin E. P. Seligman, and Lyle H. Ungar. 2013. Personality, gender, and age in the language of s...

work page 2013

[15] [15]

Country” refers to TUSC-Country and “City

Emotion regulation and psychopathol- ogy.Annual review of clinical psychology, 11:379—405. Robert S Stawski, Martin J Sliwinski, David M Almeida, and Joshua M Smyth. 2008. Reported exposure and emotional reactivity to daily stres- sors: the roles of adult age and global perceived stress.Psychology and aging, 23(1):52—61. ArthurA.Stone,JosephE.Schwartz,Joa...

work page 2008